Screening method of plasma metabonomic biomarkers for aiding diagnosis of depression and use of plasma metabonomic biomarkers

Abstract

The application belongs to the technical field of biological detection, and particularly relates to a screening method of a plasma metabolite combination marker for assisting in diagnosing depression and the use of the plasma metabolite combination marker. The application provides a combination of metabolites in plasma and the use thereof in assisting in diagnosing depression. Experimental results show that a specific plasma metabolite combination (containing tryptophan, inosine and acetyl carnitine) has the value of diagnosing or assisting in diagnosing depression, and has a good application prospect in rapid screening, risk assessment and assisting in diagnosing depression.

Description

Technical Field

[0001] This invention belongs to the field of biological detection technology, specifically relating to a screening method for plasma metabolic biomarkers used to assist in the diagnosis of depression and the applications of plasma metabolic biomarkers. Background Technology

[0002] Depression is a common and serious mental disorder that has become a major challenge in global public health. Currently, in clinical practice, the diagnosis of depression mainly relies on interview assessments conducted by clinicians based on the Diagnostic and Statistical Manual of Mental Disorders (DSM) or the International Classification of Diseases (ICD), combined with quantitative scoring using subjective assessment tools such as the Hamilton Depression Rating Scale (HAM-D), the Beck Depression Rating Scale (BDI), or the Patient Health Questionnaire-9 (PHQ-9). In recent years, researchers have attempted to use objective biological indicators to aid diagnosis, discovering a correlation between the levels of certain metabolites in the blood and depressive states, such as the inflammatory cytokine IL-6, brain-derived neurotrophic factor BDNF, tryptophan and its metabolic pathway products (such as kynurenine and 5-hydroxyindoleacetic acid), γ-aminobutyric acid (GABA), and lipid metabolites (such as ceramides). Furthermore, metabolomics methods based on targeted mass spectrometry have been used to analyze the blood metabolite profiles of patients with depression, showing potential in distinguishing patients with depression from healthy controls.

[0003] However, existing diagnostic technologies still have significant shortcomings. First, subjective assessment methods rely heavily on patient self-reporting and physician clinical experience, and are easily influenced by factors such as patient emotional state, cognitive biases, cultural background, and social expectations, resulting in poor consistency and accuracy of diagnostic results. The diagnostic accuracy rate of general practitioners is only 57.9%-73.1%, with a missed diagnosis rate as high as 73.6%. Second, while detection methods using single biomarkers or simple combinations have good objectivity, they lack sensitivity and have poor discriminatory power.

[0004] Therefore, there is an urgent need to develop a novel method for detecting depression based on combinations of blood metabolites. By screening combinations of metabolic biomarkers with high sensitivity and correlation, this method can effectively identify suspected patients, assist in clinical scale diagnosis, and provide reliable technical support for the early screening and treatment monitoring of depression. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for screening plasma metabolic biomarkers for the auxiliary diagnosis of depression, as well as the uses of plasma metabolic biomarkers.

[0006] This invention provides the use of a detection reagent for plasma metabolic biomarkers in the preparation of a depression screening kit, wherein the plasma metabolic biomarkers consist of metabolites from any of the following groups: Group 1: tryptophan, inosine, acetylcarnitine, kynurenic acid, kynurenine, glutamic acid, creatinine, serine and methionine (combinations in the following experimental examples (16)); Group 2: tryptophan, inosine, acetylcarnitine, kynurenic acid, kynurenine, asparagine, phenylalanine, glutamic acid and creatinine (combination (17) in the following experimental examples).

[0007] Furthermore, the plasma metabolic biomarker may also consist of metabolites from any of the following groups: Group 3: Tryptophan, inosine and acetylcarnitine (combination (3) in the following experimental examples); Group 4: Tryptophan, inosine, acetylcarnitine and kynurenine (combination (5) in the following experimental examples); Group 5: Tryptophan, inosine, acetylcarnitine and kynurenic acid (combination (6) in the following experimental examples); Group 6: Tryptophan, kynurenic acid, kynurenine, inosine, creatinine (combination (9) in the following experimental examples).

[0008] Preferably, the plasma metabolic biomarker consists of the following metabolites: Group 2: tryptophan, inosine, acetylcarnitine, kynurenic acid, kynurenine, asparagine, phenylalanine, glutamic acid and creatinine (combination (17) in the following experimental examples).

[0009] Preferably, the kit includes reagents selected from ELISA, chemiluminescence immunoassay, immunochromatographic assay, liquid chromatography, liquid chromatography-mass spectrometry / mass spectrometry, and spectrophotometric assay.

[0010] Preferably, the kit includes a liquid chromatography-mass spectrometry / mass spectrometry detection reagent, with mobile phase A being a 0.0025%-0.1 v / v % formic acid aqueous solution and mobile phase B being a 0.0025%-0.1 v / v % formic acid acetonitrile solution.

[0011] Preferably, the method of using the kit includes: detecting the concentration of plasma metabolic biomarkers by liquid chromatography-mass spectrometry / mass spectrometry; wherein the liquid chromatography-mass spectrometry / mass spectrometry method employs a triple quadrupole-linear ion trap composite mass spectrometry system.

[0012] Preferably, the detection reagent is a reagent for detecting the concentration of plasma metabolite biomarkers in a human plasma sample.

[0013] Preferably, the screening kit is used to help distinguish between people with depression and healthy individuals.

[0014] Preferably, the threshold for distinguishing between people with depression and healthy individuals is a preset score cutoff value.

[0015] This invention provides a method for screening plasma metabolic biomarkers for the auxiliary diagnosis of depression, comprising the following steps: Step 1: Collect plasma samples from patients with depression and healthy individuals, and preprocess the plasma samples. Step 2: Analyze the pre-processed plasma sample to obtain plasma metabolite peak data; Step 3: Compare the plasma metabolite peak data between patients with depression and healthy individuals to obtain differentially metabolites and non-differentially metabolites; Step 4: Use machine learning methods to rank the importance of differentially expressed metabolites; Step 5: Combine differentially metabolites and / or non-differentially metabolites to obtain plasma metabolic biomarkers; The plasma metabolic biomarker consists of metabolites from any of the following groups: Group 1: Tryptophan, inosine, acetylcarnitine, kynurenic acid, kynurenine, glutamic acid, creatinine, serine, methionine; Group 2: Tryptophan, inosine, acetylcarnitine, kynurenic acid, kynurenine, asparagine, phenylalanine, glutamic acid, and creatinine.

[0016] Preferably, in step 4, the machine learning method includes LASSO regression, logistic regression, random forest, support vector machine, xgboost, and boruta method.

[0017] Sensitivity indicators are particularly important for the screening and diagnosis of depression. Firstly, low sensitivity increases the probability of missed diagnoses of depression. Missed diagnoses mean patients cannot receive timely intervention, potentially missing a crucial window to prevent extreme behaviors such as suicide, a burden far outweighing the reversible burden of misdiagnosis (such as strain on medical resources). Furthermore, the "time window" for early intervention in depression is highly valuable; unrecognized depression can lead to chronicity, cognitive impairment, and social dysfunction, and each relapse increases the difficulty of treatment. In addition, patients with depression often experience stigma and decreased motivation, resulting in low rates of proactive medical intervention; a key objective of screening is to identify silent cases. The metabolite combination of this invention comprehensively considers sensitivity, accuracy, and specificity, making it suitable as a biomarker for early screening of depression.

[0018] This invention provides combinations of plasma metabolites and their use in the auxiliary diagnosis of depression. Experimental results show that specific combinations of plasma metabolites have diagnostic or auxiliary diagnostic value for depression, and show promising application prospects in rapid screening and early warning of depression.

[0019] Obviously, based on the above description of the present invention, and according to common technical knowledge and conventional methods in the field, various other modifications, substitutions or alterations can be made without departing from the basic technical concept of the present invention.

[0020] The following detailed embodiments further illustrate the above-described content of the present invention. However, this should not be construed as limiting the scope of the present invention to the following examples. All technologies implemented based on the above-described content of the present invention fall within the scope of the present invention. Attached Figure Description

[0021] Figure 1 The receiver operating characteristic (ROC) curve is shown for the metabolite combination of Example 1. Figure 2 TIC (Total Ion Current) chromatogram of human plasma labeled XJ442; Figure 3 The TIC (Total Ion Current) chromatogram of a human plasma QC sample, wherein the QC sample is a mixed quality control sample prepared by mixing equal amounts of all plasma samples to be tested; Figure 4 A graph showing the concentration and differential results of metabolites in healthy individuals and patients with depression in the training set; Figure 5 This is a ranking plot of feature importance output by lasso regression. Detailed Implementation

[0022] Unless otherwise specified, all reagents and materials used in the following examples and experimental cases are commercially available.

[0023] Example 1: Metabolite combination and detection kit for depression biomarkers I. Metabolite Combination This embodiment provides a combination of metabolites for use as biomarkers of depression, specifically: Tryptophan + Inosine + Acetylcarnitine + Kynurenic acid + Kynurenine + Asparagine + Phenylalanine + Glutamicacid + Creatinine. By detecting the concentrations of these plasma metabolites, it is possible to more accurately diagnose or assist in the diagnosis of depression.

[0024] II. Reagent Kit This embodiment provides a detection kit for detecting metabolites in plasma using liquid chromatography-mass spectrometry / mass spectrometry (LC-MS / MS). The kit consists of methanol, a 0.1% aqueous solution of formic acid (FA), a 0.1% FA solution in acetonitrile, and standards for each metabolite.

[0025] III. Instructions for using the reagent kit 1. Plasma sample pretreatment (1) Take 5 μL of plasma sample and place it in a new 1.5 mL EP tube. Add 500 μL of methanol pre-cooled at -20 ℃ and vortex mix for 5 s. (2) Ultrasound in an ice-water bath for 30 min; (3) Centrifuge again at 12000 rpm and 4℃ for 10 min; (4) Take 500 μL of supernatant and place it in another new 1.5 mL EP tube, concentrate and dry at 37℃; (5) Add 100 μL of pre-cooled 0.1% FA aqueous solution to reconstitute; (6) After centrifuging at 12000 rpm and 4℃ for 10 min, take the supernatant for testing and analysis.

[0026] 2. Detection Method Metabolites were detected in pretreated plasma samples using LC-MS / MS, specifically a QTRAP 6500+ LC-MS / MS system. This system is a triple quadrupole-linear ion trap combined mass spectrometry system, combining the quantitative capabilities of triple quadrupole mass spectrometry with the qualitative advantages of linear ion trap mass spectrometry. The specific detection methods and conditions are as follows: 2.1 Liquid chromatography detection method: (1) Mobile phase A is a 0.1% (v / v) FA aqueous solution, mobile phase B is a 0.1% (v / v) FA acetonitrile solution, and the flow rate is 0.3 mL / min; (2) Mobile phase elution gradient: 0 min, 98% A, 2% B; 1 min, 98% A, 2% B; 4 min, 30% A, 70% B; 4.5 min, 2% A, 98% B; 7.5 min, 2% A, 98% B; 7.6 min, 2% A, 98% B; 10 min, 98% A, 2% B.

[0027] 2.2 Mass Spectrometry Methods: (1) Instrument model: SCIEX QTRAP 6500+, equipped with IonDrive Turbo V ion source.

[0028] (2) Ion source parameters (3) Detection of target metabolites The concentrations of Tryptophan, Inosine, Acetylcarnitine, Kynurenine, Asparagine, Phenylalanine, Glutamic acid, and Creatinine were measured separately. The plasma concentrations of these nine metabolites differed significantly between patients with depression and healthy controls (P < 0.05). The average concentrations of glutamate, inosine, creatinine, and phenylalanine were elevated in patients with depression, while the average concentrations of asparagine, tryptophan, acetylcarnitine, kynurenine, and kynurenine were decreased.

[0029] The combined algorithm achieved an average AUC of 0.8243, an average sensitivity of 0.7765, and an average specificity of 0.8046 on the training set used in the following experimental examples. On the independent validation set (the test set for the following experimental examples), the combined algorithm achieved an AUC of 0.7956, a sensitivity of 0.7846, and a specificity of 0.7679. The ROC curves are shown below. Figure 1 As shown, the optimal cutoff value was found through Yoden's optimum in ROC analysis to distinguish between patients with depression and healthy individuals.

[0030] Example 2: Metabolite combination and detection kit for depression biomarkers The kit and detection method described in Example 1 differed in that the metabolite combination consisted of tryptophan, inosine, acetylcarnitine, kynurenic acid, kynurenine, glutamic acid, creatinine, serine, and methionine. This metabolite combination showed an average AUC of 0.8041, an average sensitivity of 0.7372, and an average specificity of 0.8111 in the training set used in the following experimental examples. In the independent validation set (the test set of the following experimental examples), the combination showed an AUC of 0.7876, a sensitivity of 0.5538, and a specificity of 0.9286.

[0031] The technical solution of the present invention will be further explained through experiments below.

[0032] The plasma samples used in the following experimental cases were obtained from: 211 healthy controls (female / male: 151 / 60) and 195 patients with depression (female / male: 149 / 46). The diagnosis of depression was confirmed using the Hamilton Depression Rating Scale (HAMD-17). All patients with mental illness were recruited from the Affiliated Three Gorges Hospital of Chongqing University, while samples from healthy controls were obtained from the First Affiliated Hospital of Chongqing Medical University. The subjects were divided into training and testing sets at a ratio of 7:3. Demographic information is shown in Table 1.

[0033] Table 1 Note: BMI represents Body Mass Index, and HAMD-17 represents the average score of the Hamilton Depression Rating Scale.

[0034] Experimental Example 1 was used to assist in the screening of metabolites for depression. I. Experimental Methods 1. Identification of candidate metabolites The applicant has disclosed 6,365 human metabolites associated with depression in the publicly available global protein and metabolite network database of depression (ProMENDA, https: / / menda.cqmu.edu.cn). This experimental case further screened for potential candidate metabolites related to depression.

[0035] The screening method for candidate plasma metabolites in the ProMENDA database is as follows: (1) A study comparing human patients with depression with healthy controls; (2) The research sample was derived from blood; (3) Count the number of times each metabolite was reported as dysregulated (upregulated or downregulated) in the disease, and retain only metabolites that were reported as dysregulated in at least 4 studies; (4) Perform vote-counting analysis on the retained metabolites. Metabolites with p-value < 0.05 are the final candidate metabolites.

[0036] 2. Verification of significant differences between groups Following the method of use of the kit in Example 1, the concentrations of candidate metabolites were detected in subjects, and the sum of all metabolite concentrations for each sample was normalized. To meet the assumptions of the subsequent statistical model regarding data distribution, the normalized concentration data were logarithmically transformed to eliminate the influence of differences in the dimensions and concentration ranges of different metabolites on statistical tests and diagnostic models. After the above rigorous data processing procedure, a standardized concentration matrix of behavioral samples, listed as metabolites, was obtained.

[0037] In the training set, the Wilcox test was used to verify the concentration differences of candidate biomarkers in the depression group relative to healthy controls. Metabolites with a p-value < 0.05 were selected as candidate core biomarkers, and the remaining metabolites were selected as candidate auxiliary biomarkers.

[0038] 3. Assessment of diagnostic capabilities (1) Use the pROC package in R language to evaluate the individual diagnostic performance of candidate core biomarkers in the training set.

[0039] (2) LASSO regression experiment: The importance of core biomarker features for depression was calculated in the training set using the LASSO regression method. LASSO regression was implemented using the glmnet package in R language. The alpha parameter is set to 1 (corresponding to LASSO regression, achieving feature shrinkage and selection). The optimal lambda value is determined through 10-fold cross-validation (10-CV), with lambda.min (the lambda value that minimizes the cross-validation error) as the selection criterion. The corresponding clinical diagnostic labels are used as the response variable Y (depression = 1, healthy controls = 0) to construct the LASSO regression model, and the importance of candidate metabolites in the model is calculated based on lambda.min.

[0040] Based on the importance of the features, candidate core biomarkers and candidate auxiliary biomarkers are combined to construct metabolite combinations, as shown in Table 4.

[0041] The biomarker combinations were subjected to 200 repeated 5-fold cross-validation trials using the training set (4 folds as the internal training set and 1 fold as the internal test set, for a total of 1000 trials). The mean AUC was calculated to evaluate the diagnostic potential of different biomarker combinations.

[0042] Generally, the area under the receiver operating characteristic (AUC) curve is used to assess a biomarker's ability to distinguish target diseases (such as depression) from healthy individuals. Generally speaking: an AUC of 0.5 indicates no discriminatory power (equivalent to random guessing); an AUC between 0.5 and 0.6 indicates a lack of clinical diagnostic discrimination; an AUC between 0.6 and 0.7 indicates low discriminatory power; an AUC between 0.7 and 0.8 indicates some accuracy and preliminary clinical diagnostic reference value; and an AUC between 0.8 and 0.9 indicates good discriminatory accuracy, suggesting significant clinical diagnostic potential.

[0043] II. Experimental Results 1. Identification of candidate metabolites This experiment preliminarily screened out 13 candidate metabolites that may be potentially associated with depression, as follows: Tryptophan, Inosine, Acetylcarnitine, Kynurenic acid, Kynurenine, Asparagine, Phenylalanine, Glutamic acid, Creatinine, Methionine, Valine, Serine, and Glutamine.

[0044] 2. Results of differences between groups Total ion current (TIC) plot of human plasma sample as shown in Figure Figure 2-3 As shown in the figure. Using the LC-MS / MS method, 13 metabolites were measured in plasma samples from healthy individuals and individuals with depression in the training set. The differential concentrations of each metabolite in the two populations were calculated, and the results are shown in the figure. Figure 4 As shown in Table 2.

[0045] Table 2 Among the above metabolites, the plasma metabolites: tryptophan, inosine, acetylcarnitine, kynurenine, asparagine, phenylalanine, glutamate, and creatinine showed significant differences between healthy and depressed individuals and can be considered as candidate core biomarkers; methionine, valine, serine, and glutamine had p-values ​​> 0.05 and can be considered as candidate auxiliary biomarkers.

[0046] 3. Diagnostic potential results of individual metabolites The individual diagnostic power of the candidate metabolites is shown in Table 3. The AUCs of the single metabolites, from highest to lowest, are: Kynurenine, Creatinine, Inosine, Tryptophan, Glutamic acid, Phenylalanine, Asparagine, and Acetylcarnitine. The results indicate that the AUCs of these candidate metabolites in the training set are all less than 0.7, and none of them possess the potential for individual diagnosis.

[0047] Table 3 4. Diagnostic potential results of metabolite combinations (1) Results of LASSO regression experiment The results of the LASSO regression are as follows: Figure 5 As shown in the figure. The results indicate that the metabolite characteristics, from most important to least important, are: Inosine, Tryptophan, Kynurenine, Acetylcarnitine, Kynuurenic acid, Asparagine, Glutamic acid, Phenylalanine, and Creatinine.

[0048] (2) Results of 200 repeated 5-fold cross-validation This experiment screened combinations of metabolites, and their diagnostic performance results are shown in Table 4. The results showed that the average AUC values ​​of repeated cross-validation for metabolite combinations (1)-(9), (12), (13), (15)-(17) in the training set were greater than 0.7-0.8. Among them, the average AUC values ​​of metabolite combinations (5), (7), (16), and (17) were all greater than 0.8, significantly higher than other metabolite combinations. Moreover, the sensitivity of these preferred combinations was also significantly higher than other combinations, greater than 0.72. This suggests that these metabolite combinations have significant potential for clinical auxiliary screening and diagnosis, with metabolite combination (17) showing better detection performance, with an average AUC of 0.8243.

[0049] Table 4 The above results indicate that individual candidate metabolites do not have diagnostic potential. Combining candidate metabolites, the average AUC of the training set corresponding to metabolite combinations (1)-(9), (12), (13), (15)-(17) is in the range of 0.7-0.85, which has the potential to assist in the diagnosis of depression. In particular, the average AUC and sensitivity of metabolite combinations (5), (7), (16) and (17) are significantly higher than those of other combinations, showing good discriminative potential and sensitivity.

[0050] Experimental Example 2: Independent Validation of Diagnostic Biomarkers I. Experimental Methods The test set was used to individually validate the combination of biomarkers with a mean AUC > 0.7 and good diagnostic potential in Experiment 1. Receiver operating characteristic (ROC) curves were plotted and the area under the curve (AUC) was calculated. The optimal cut-off value for the risk score was determined using the Youden's Index maximization method. The Youden's Index formula is as follows: Youden's Index=Sensitivity+Specificity−1 Based on the optimal cutoff value, the complete validation set samples are divided into "high risk (predicted as depression)" and "low risk (predicted as healthy control)". A confusion matrix is ​​constructed to record the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). Based on the confusion matrix, the sensitivity (Sen) is calculated as Sen=TP / (TP+FN), the specificity (Spe) is calculated as Spe=TN / (FP+TN), and the accuracy (Accuracy) is calculated as Acc=(TP+TN) / (FP+FN+TP+TN).

[0051] II. Experimental Results This experiment individually validated the diagnostic capability of biomarker combinations with high diagnostic potential selected through repeated cross-validation on a single test set. The diagnostic performance results are shown in Table 5. The results show that, in both the test and training sets, the AUC values ​​of metabolite combinations (16)-(17) were significantly higher than those of other metabolite combinations, demonstrating good discrimination accuracy and significant potential for clinical auxiliary diagnosis.

[0052] Table 5 Moreover, considering the accuracy and sensitivity in both the test and training sets, the AUC and sensitivity of metabolite combinations (3), (5), (6), (9), and (17) were all greater than 0.7, significantly higher than other combinations. Among them, the AUC and sensitivity of metabolite combination (17) were significantly higher than other groups in both datasets, suggesting that these preferred combinations have good generalization ability, good accuracy and sensitivity, and are expected to be further translated into clinical applications for early screening of depression, avoiding the harm caused by missed diagnoses.

[0053] As can be seen from the above embodiments and experimental examples, the present invention provides a combination of plasma metabolites and their use in the auxiliary diagnosis of depression. Experimental results show that specific combinations of plasma metabolites have diagnostic or auxiliary diagnostic value for depression, and show good application prospects in rapid screening and early warning of depression.

Claims

1. The use of a reagent for detecting plasma metabolic biomarkers in the preparation of a depression screening kit, characterized in that, The plasma metabolic biomarker consists of metabolites from any of the following groups: Group 1: Tryptophan, inosine, acetylcarnitine, kynurenic acid, kynurenine, glutamic acid, creatinine, serine, and methionine; Group 2: Tryptophan, inosine, acetylcarnitine, kynurenic acid, kynurenine, asparagine, phenylalanine, glutamic acid, and creatinine.

2. The use according to claim 1, characterized in that, The plasma metabolic biomarkers consist of the following metabolites: Group 2: Tryptophan, inosine, acetylcarnitine, kynurenic acid, kynurenine, asparagine, phenylalanine, glutamic acid, and creatinine.

3. The use according to claim 1, characterized in that, The kit includes reagents selected from ELISA, chemiluminescence immunoassay, immunochromatographic assay, liquid chromatography, liquid chromatography-mass spectrometry / mass spectrometry, and spectrophotometric assays.

4. The use according to claim 3, characterized in that, The kit includes a liquid chromatography-mass spectrometry / mass spectrometry detection reagent, with mobile phase A being a 0.0025%-0.1 v / v % formic acid aqueous solution and mobile phase B being a 0.0025%-0.1 v / v % formic acid acetonitrile solution.

5. The use according to claim 4, characterized in that, The method of using the kit includes: detecting the concentration of plasma metabolic biomarkers by liquid chromatography-mass spectrometry / mass spectrometry; the liquid chromatography-mass spectrometry / mass spectrometry method uses a triple quadrupole-linear ion trap composite mass spectrometry system.

6. The use according to claim 1, characterized in that: The detection reagent is a reagent for detecting the concentration of plasma metabolite biomarkers in human plasma samples.

7. The use according to claim 1, characterized in that: The screening kit is used to help distinguish between people with depression and healthy individuals.

8. The use according to claim 7, characterized in that: The threshold for distinguishing between people with depression and healthy individuals is a preset score cutoff value.

9. A method for screening plasma metabolic biomarkers for the auxiliary diagnosis of depression, characterized in that, Includes the following steps: Step 1: Collect plasma samples from patients with depression and healthy individuals, and preprocess the plasma samples. Step 2: Analyze the pre-processed plasma sample to obtain plasma metabolite peak data; Step 3: Compare the plasma metabolite peak data between patients with depression and healthy individuals to obtain differentially metabolites and non-differentially metabolites; Step 4: Use machine learning methods to rank the importance of differentially expressed metabolites; Step 5: Combine differentially metabolites and / or non-differentially metabolites to obtain plasma metabolic biomarkers; The plasma metabolic biomarker consists of metabolites from any of the following groups: Group 1: Tryptophan, inosine, acetylcarnitine, kynurenic acid, kynurenine, glutamic acid, creatinine, serine, and methionine; Group 2: Tryptophan, inosine, acetylcarnitine, kynurenic acid, kynurenine, asparagine, phenylalanine, glutamic acid, and creatinine.

10. The method for screening plasma metabolic biomarkers for the auxiliary diagnosis of depression according to claim 9, characterized in that, In step 4, the machine learning methods include LASSO regression, logistic regression, random forest, support vector machine, xgboost, and boruta method.

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